Propagation of pressure drop in coalbed methane reservoir during drainage stage

 Numerical simulation was employed to investigate the propagation speed of pressure drop at the drainage stage in coalbed methane (CBM) reservoirs. A seepage model of single-phase water in CBM reservoirs was generated with the parameter from CBM well ZS39 in the Zhengzhuang block of the southern Qinshui Basin. The effects of stress sensitivity and reservoir properties on the pressure drop propagation process were analysed. Moreover the pressure drop funnel scale index was introduced to quantitatively characterize the propagation process. The results indicate that stress sensitivity cause the permeability form the permeability drop funnel, which is consistent with the shape of the pressure drop funnel. Under the constant bottom pressure, the propagation speed of the funnel will gradually decrease in both longitudinal and lateral direction. And the overall propagation speed rapidly increases first and then gradually decreases. In the scenario of steady decrease in the bottomhole pressure, the pressure drop speed shows an increasing trend in the longitudinal direction, and a decreasing trend in the lateral direction. The overall propagation speed of the pressure drop funnel increases all along. The reservoir pressure drop is positively correlated with the initial porosity, the initial permeability and the elastic modulus. For Poisson ratio, when the ratio is small, the reservoir pressure drop has a negative correlation. As Poisson ratio increases over 0.35, a positive correlation exists. It was found from the sensitivity analysis of reservoir pressure drop that petrophysical parameters have strong sensitivity to pressure drop, especially for permeability. Therefore, this work may provide insights into the CBM reservoir properties, and thus will be favorable for improving CBM recovery.Cited as: Jia, D., Qiu, Y., Li, C, Cai, Y. Propagation of pressure drop in coalbed methane reservoir during drainage stage. Advances in Geo-Energy Research, 2019, 3(4): 387-395, doi: 10.26804/ager.2019.04.0

Scale-span pore structure heterogeneity of high volatile bituminous coal and anthracite by FIB-SEM and X-ray μ-CT

This research was funded by the National Natural Science Foundation of China (grant nos.41830427, 41922016, and 41772160) and the Fundamental Research Funds for Central Universities (grant no. 2652018002).Peer reviewedPostprin

An Improved Modeling for Low-grade Organic Rankine Cycle Coupled with Optimization Design of Radial-inflow Turbine

This document is the Accepted Manuscript of the following article: Lijing Zhai, Guoqiang Xu, Jie Wen, Yongkai Quan, Jian Fu, Hongwei Wu, and Tingting Li, ‘An improved modeling for low-grade organic Rankine cycle coupled with optimization design of radial-inflow turbine’, Energy Conversion and Management, Vol. 153: 60-70, December 2017. Under embargo. Embargo end date: 10 October 2018. The final, published version is available online at DOI: https://doi.org/10.1016/j.enconman.2017.09.063. Published by Elsevier Ltd.Organic Rankine cycle (ORC) has been proven to be an effective and promising technology to convert low-grade heat energy into power, attracting rapidly growing interest in recent years. As the key component of the ORC system, turbine significantly influences the overall cycle performance and its efficiency also varies with different working fluids as well as in different operating conditions. However, turbine efficiency is generally assumed to be constant in the conventional cycle design. Aiming at this issue, this paper couples the ORC system design with the radial-inflow turbine design to investigate the thermodynamic performance of the ORC system and the aerodynamic characteristics of radial-inflow turbine simultaneously. The constrained genetic algorithm (GA) is used to optimize the radial-inflow turbine with attention to six design parameters, including degree of reaction, velocity ratio, loading coefficient, flow coefficient, ratio of wheel diameter, and rotational speed. The influence of heat source outlet temperature on the performance of the radial-inflow turbine and the ORC system with constant mass flow rate of the heat source and constant heat source inlet temperature is investigated for four kinds of working fluids. The net electrical powers achieved are from few tens kWs to one hundred kWs. The results show that the turbine efficiency decreases with increasing heat source outlet temperature and that the decreasing rate of turbine efficiency becomes faster in the high temperature region. The optimized turbine efficiency varies from 88.06% (using pentane at the outlet temperature of 105 ºC) to 91.01% (using R245fa at the outlet temperature of 80 ºC), which appears much higher compared to common values reported in the literature. Furthermore, the cycle efficiency increases monotonously with the growth of the heat source outlet temperature, whereas the net power output has the opposite trend. R123 achieves the maximum cycle efficiency of 12.21% at the heat source outlet temperature of 110 ºC. Based on the optimized results, the recommended ranges of the key design parameters for ORC radial-inflow turbine are presented as well.Peer reviewe

SiDA: Sparsity-Inspired Data-Aware Serving for Efficient and Scalable Large Mixture-of-Experts Models

Mixture-of-Experts (MoE) has emerged as a favorable architecture in the era of large models due to its inherent advantage, i.e., enlarging model capacity without incurring notable computational overhead. Yet, the realization of such benefits often results in ineffective GPU memory utilization, as large portions of the model parameters remain dormant during inference. Moreover, the memory demands of large models consistently outpace the memory capacity of contemporary GPUs. Addressing this, we introduce SiDA (Sparsity-inspired Data-Aware), an efficient inference approach tailored for large MoE models. SiDA judiciously exploits both the system's main memory, which is now abundant and readily scalable, and GPU memory by capitalizing on the inherent sparsity on expert activation in MoE models. By adopting a data-aware perspective, SiDA achieves enhanced model efficiency with a neglectable performance drop. Specifically, SiDA attains a remarkable speedup in MoE inference with up to 3.93X throughput increasing, up to 75% latency reduction, and up to 80% GPU memory saving with down to 1% performance drop. This work paves the way for scalable and efficient deployment of large MoE models, even in memory-constrained systems

An external capacitor-less low-dropout voltage regulator using a transconductance amplifier

This paper presents an external capacitor-less NMOS low-dropout (LDO) voltage regulator integrated with a standard CSMC 0.6 μm BiCMOS technology. Over a -55 ∘C to +125 ∘C temperature range, the fabricated LDO provides a stable and considerable amount of 3 A output current over wide ranges of output capacitance COUT (from zero to hundreds of μF ) and effective-series-resistance (ESR) (from tens of milliohms to several ohms). A low dropout voltage of 200 mV has been realised by accurate modelling. Operating with an input voltage ranging from 2.2 V to 5.5 V provides a scalable output voltage from 0.8 V to 3.6 V. When the load current jumps from 100 mA to 3 A within 3 μs, the output voltage overshoot remains as low as 50 mV without output capacitance, COUT. The system bandwidth is about 2 MHz, and hardly changes with load altering to ensure system stability. To improve the load transient response and driving capacity of the NMOS power transistor, a buffer with high input impedance and low output impedance is applied between the transconductance amplifier and the NMOS power transistor. The total area of fabricated LDO voltage regulator chip including pads is 2.1 mm×2.2 mm